Bottom Line:
Studies using multivariate pattern analysis have shown the potential value in clinically diagnosing psychiatric disorders with neuroimaging data.Regions showing different discriminating values between diagnostic groups were mainly located in default mode network, dorsal attention network, self-referential network, and sensory networks, while the left medial prefrontal cortex was identified with the highest weight.These results implicate that ReHo has good diagnostic potential in SAD, and thus may provide an initial step towards the possible use of whole brain local connectivity to inform the clinical evaluation.

ABSTRACTAlthough decades of efforts have been spent studying the pathogenesis of social anxiety disorder (SAD), there are still no objective biological markers that could be reliably used to identify individuals with SAD. Studies using multivariate pattern analysis have shown the potential value in clinically diagnosing psychiatric disorders with neuroimaging data. We therefore examined the diagnostic potential of regional homogeneity (ReHo) underlying neural correlates of SAD using support vector machine (SVM), which has never been studied. Forty SAD patients and pairwise matched healthy controls were recruited and scanned by resting-state fMRI. The ReHo was calculated as synchronization of fMRI signals of nearest neighboring 27 voxels. A linear SVM was then adopted and allowed the classification of the two groups with diagnostic accuracy of ReHo that was 76.25% (sensitivity = 70%, and specificity = 82.5%, P ≤ 0.001). Regions showing different discriminating values between diagnostic groups were mainly located in default mode network, dorsal attention network, self-referential network, and sensory networks, while the left medial prefrontal cortex was identified with the highest weight. These results implicate that ReHo has good diagnostic potential in SAD, and thus may provide an initial step towards the possible use of whole brain local connectivity to inform the clinical evaluation.

Mentions:
The classification of the two groups with overall diagnostic accuracy of ReHo maps was 76.25% (sensitivity = 70% and specificity = 82.5%, P ≤ 0.001) achieved by SVM (Figure 1). The set of regions showed different value between the diagnostic groups mainly located in frontal, temporal, and occipital regions (Figure 2, Table 2). In the discrimination map, a positive value means a relative higher weight in SAD (red scale) and helps in the identification of individuals with SAD, with regions mainly located at right orbitofrontal gyrus (OFG), right middle frontal gyrus, right pars triangularis, right superior temporal gyrus (STG), left middle temporal gyrus (MTG), right postcentral gyrus (PCG), left inferior parietal lobe (IPL), and right precuneus, while a negative value means a relative higher weight in healthy controls and contributes to the identification of healthy subjects, locating in left medial prefrontal cortex (mPFC), bilateral middle frontal gyrus (MFG), right inferior occipital gyrus (IOG), and right cuneus (Figure 2).

Mentions:
The classification of the two groups with overall diagnostic accuracy of ReHo maps was 76.25% (sensitivity = 70% and specificity = 82.5%, P ≤ 0.001) achieved by SVM (Figure 1). The set of regions showed different value between the diagnostic groups mainly located in frontal, temporal, and occipital regions (Figure 2, Table 2). In the discrimination map, a positive value means a relative higher weight in SAD (red scale) and helps in the identification of individuals with SAD, with regions mainly located at right orbitofrontal gyrus (OFG), right middle frontal gyrus, right pars triangularis, right superior temporal gyrus (STG), left middle temporal gyrus (MTG), right postcentral gyrus (PCG), left inferior parietal lobe (IPL), and right precuneus, while a negative value means a relative higher weight in healthy controls and contributes to the identification of healthy subjects, locating in left medial prefrontal cortex (mPFC), bilateral middle frontal gyrus (MFG), right inferior occipital gyrus (IOG), and right cuneus (Figure 2).

Bottom Line:
Studies using multivariate pattern analysis have shown the potential value in clinically diagnosing psychiatric disorders with neuroimaging data.Regions showing different discriminating values between diagnostic groups were mainly located in default mode network, dorsal attention network, self-referential network, and sensory networks, while the left medial prefrontal cortex was identified with the highest weight.These results implicate that ReHo has good diagnostic potential in SAD, and thus may provide an initial step towards the possible use of whole brain local connectivity to inform the clinical evaluation.

ABSTRACTAlthough decades of efforts have been spent studying the pathogenesis of social anxiety disorder (SAD), there are still no objective biological markers that could be reliably used to identify individuals with SAD. Studies using multivariate pattern analysis have shown the potential value in clinically diagnosing psychiatric disorders with neuroimaging data. We therefore examined the diagnostic potential of regional homogeneity (ReHo) underlying neural correlates of SAD using support vector machine (SVM), which has never been studied. Forty SAD patients and pairwise matched healthy controls were recruited and scanned by resting-state fMRI. The ReHo was calculated as synchronization of fMRI signals of nearest neighboring 27 voxels. A linear SVM was then adopted and allowed the classification of the two groups with diagnostic accuracy of ReHo that was 76.25% (sensitivity = 70%, and specificity = 82.5%, P ≤ 0.001). Regions showing different discriminating values between diagnostic groups were mainly located in default mode network, dorsal attention network, self-referential network, and sensory networks, while the left medial prefrontal cortex was identified with the highest weight. These results implicate that ReHo has good diagnostic potential in SAD, and thus may provide an initial step towards the possible use of whole brain local connectivity to inform the clinical evaluation.